Recommender systems use algorithms to provide users with product or service
recommendations. Recently, these systems have been using machine learning algorithms …

There exist situations of decision-making under information overload in the Internet, where
people have an overwhelming number of available options to choose from, eg products to …

Link recommendation has attracted significant attention from both industry practitioners and
academic researchers. In industry, link recommendation has become a standard and most …

Reciprocal recommender system (RRS), considering a two-way matching between two
parties, has been widely applied in online platforms like online dating and recruitment …

With the development of social networking applications, microblog has turned to be an
indispensable online communication network in our daily life. For microblog users …

Distinct social networks are interconnected via membership overlap, which plays a key role
when crossing information is investigated in the context of multiple-social-network analysis …

Many social networks in our daily life are bipartite networks built on reciprocity. How can we
make recommendations to others so that the user is interested in and attractive to those …

A typical recommender setting is based on two kinds of relations: similarity between users
(or between objects) and the taste of users towards certain objects. In environments such as …

The social web is characterized by a wide variety of connections between individuals and
entities. A challenge for recommendation is to represent and synthesize all useful aspects of …

Reciprocal recommender systems (RRS) have been widely used in online platforms such as
online dating and recruitment. They can simultaneously fulfill the needs of both parties …